Abstract
The single-trial classification of neural responses to stimuli is an essential element of non-invasive brain-machine interfaces (BMI) based on the electroencephalogram (EEG). However, typically, these stimuli are artificial and the classified neural responses only indirectly related to the content of the stimulus. Fixation-related potentials (FRP) promise to overcome these limitations by directly reflecting the content of visual information that is perceived. We present a novel approach for discriminating between single-trial FRP related to fixations on objects versus on a plain background. The approach is based on a source power decomposition that exploits fixation parameters as target variables to guide the optimization. Our results show that this method is able to classify object versus non-object epochs with a much better accuracy than reported previously. Hence, we provide a further step to exploiting FRP for more versatile and natural BMI.
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Acknowledgments
This research/work was partially supported by the Cluster of Excellence Cognitive Interaction Technology ‘CITEC’ (EXC 277) at Bielefeld University, which is funded by the German Research Foundation (DFG).
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Finke, A., Ritter, H. (2016). Discriminating Object from Non-object Perception in a Visual Search Task by Joint Analysis of Neural and Eyetracking Data. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9948. Springer, Cham. https://doi.org/10.1007/978-3-319-46672-9_61
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DOI: https://doi.org/10.1007/978-3-319-46672-9_61
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